Hyperspectral image unsupervised classification by robust manifold matrix factorization
作者: Lefei ZhangLiangpei ZhangBo DuJane YouDacheng Tao
作者单位: 1School of Computer Science, Wuhan University, Wuhan 430079, PR China
2State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, PR China
3Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
4UBTECH Sydney Artificial Intelligence Centre and the School of Information Technologies, Faculty of Engineering and Information Technologies, The University of Sydney, NSW 2008, Australia
刊名: Information Sciences, 2019, Vol.485 , pp.154-169
来源数据库: Elsevier Journal
DOI: 10.1016/j.ins.2019.02.008
关键词: Hyperspectral imageData clusteringDimensionality reductionMatrix factorizationManifold regularization
原始语种摘要: Abstract(#br)Hyperspectral remote sensing image unsupervised classification, which assigns each pixel of the image into a certain land-cover class without any training samples, plays an important role in the hyperspectral image processing but still leaves huge challenges due to the complicated and high-dimensional data observation. Although many advanced hyperspectral remote sensing image classification techniques based on supervised and semi-supervised learning had been proposed and confirmed effective in recent years, they require a certain number of high quality training samples to learn a classifier, and thus can’t work in the unsupervised manner. In this work, we propose a hyperspectral image unsupervised classification framework based on robust manifold matrix factorization and its...
全文获取路径: Elsevier  (合作)
影响因子:3.643 (2012)

  • factorization 因式分解
  • image 
  • robust 牢固的
  • manifold 多样性
  • regularization 正则化
  • clustering 聚类
  • dimensionality 量纲
  • propose 提议
  • existing 现行
  • matrix 石基